I started with ingestion and worked toward storage and querying, which felt logical but I think I spent way too long on the front end of the pipeline and never got to the analytics layer in a satisfying way.
Start by clarifying requirements and scale (e.g., events per second, latency, data retention) to frame the design. Then propose a high-level architecture with ingestion, processing, storage, and serving layers, and dive into key components like Kafka, Flink, and S3. Finally, discuss trade-offs around consistency, cost, and scalability, and how you would monitor and evolve the system.
Pro tip: Emphasize data partitioning and backpressure handling early, as these are critical for large-scale streaming systems and often overlooked. Also, relate your design to real-world constraints like cost and operational complexity, showing you think beyond just technical components.
Ask questions to understand the expected event volume, latency requirements, data retention, and use cases (e.g., real-time analytics, personalization). This ensures your design is appropriately scoped.
Outline the main layers: ingestion (e.g., Kafka), stream processing (e.g., Flink, Spark Streaming), storage (e.g., S3, Cassandra), and serving (e.g., API, dashboards). Explain how data flows through these layers.
Pick 1-2 critical components (e.g., Kafka partitioning, Flink state management) and discuss design choices, such as partitioning strategy, fault tolerance, and exactly-once semantics.
Discuss trade-offs like latency vs. throughput, cost vs. durability, and how to scale each layer (e.g., horizontal scaling, sharding). Mention monitoring and alerting.
Recap the design, highlight how it meets requirements, and suggest potential improvements or next steps. Be open to feedback and iterate based on interviewer hints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.